Experimental evidence of reduced sticking of nanoparticles on a metal grid

Filtering of NaCl, CaCl2, (NH4)2SO4 and NiSO4 aerosol particles 7–20 nm in diameter by a stainless steel grid was studied in order to find out if there is perfect sticking or partial rebound. Our experiment used particles from a spray-drying process, the majority of which were electrically neutral. Penetration through the grid was measured by comparing the concentration downstream of the grid with the upstream concentration under otherwise identical conditions. Size selection was done with a scanning mobility particle sizer (SMPS). Filter penetration P as function of the particle diameter dp was expressed by View the MathML source . The values of x determined were smaller than the theoretical value of 1.29, indicating enhanced penetration of small particles and deviation from the classical filtration model. Because of possible systematic errors in the size selection, we focus on the differences of x from material to material, which indicate different sticking probabilities. We apply a statistical test, which yields a 90% confidence level for the result. There is a sticking probability of <100% at least for NaCl particles and even more so for NiSO4. This result is in contrast to former findings using metal and/or charged particles, and we speculate that the discrepancy is due to the smaller Hamaker constant of salts and that particle charge is important for the sticking probability

De steile opmars van drones en de uitdagingen voor de politie

Terwijl defensie en politie in Nederland en België op relatief kleine schaal met drones experimenteren, rijst intussen een andere problematiek: de steile opmars van civiele drones. Drones waren in eerste instantie ontwikkeld voor militaire doeleinden, maar evolueerden al snel tot een breed inzetbare technologie waaruit een miljardenindustrie is ontstaan. Via een beknopte en beschrijvende analyse willen we met dit artikel inzicht bieden in de technologische ontwikkelingen en de betekenis daarvan voor de politie. In dit artikel bieden we een overzicht van de bestaande drones en komen de belangrijkste controversen met betrekking tot drones en de explosieve groei van de civiele drones aan bod. Tot slot staan we stil bij de problematiek met betrekking tot de gefragmenteerde wetgeving rond drones. We baseren ons hiervoor op een recente interventie van de vicevoorzitter van de Europese Commissie , wat als een keerpunt kan beschouwd worden in de zoektocht naar een juridisch kader voor de verwachte miljardenindustrie inzake drones. Dit heeft uiteraard ook consequenties voor het politiewerk in de toekomst, zoals controles op correct gebruik van civiele drones en interventies wanneer ongevallen met drones zich voordoen. In die optiek is het van belang de politiesector intensief te betrekken in de regulering van deze nieuwe technologie, want het tijdperk van de drones staat voor de deur

Big Data Risk Assessment the 21st Century approach to safety science

Safety Science has been developed over time with notable models in the early 20th Century such as Heinrich’s iceberg model and the Swiss cheese model. Common techniques such fault tree and event tree analyses, HAZOP analysis and bow-ties construction are widely used within industry. These techniques are based on the concept that failures of a system can be caused by deviations or individual faults within a system, combinations of latent failures, or even where each part of a complex system is operating within normal bounds but a combined effect creates a hazardous situation. In this era of Big Data, systems are becoming increasingly complex, producing such a large quantity of data related to safety that cannot be meaningfully analysed by humans to make decisions or uncover complex trends that may indicate the presence of hazards. More subtle and automated techniques for mining these data are required to provide a better understanding of our systems and the environment within which they operate, and insights to hazards that may not otherwise be identified. Big Data Risk Analysis (BDRA) is a suite of techniques being researched to identify the use of non-traditional techniques from big data sources to predict safety risk. This paper describes early trials of BDRA that have been conducted on railway signal information and text-based reports of railway safety near misses and the ongoing research that is looking at combining various data sources to uncover obscured trends that cannot be identified by considering each source individually. The paper also discusses how visual analytics may be a key tool in analysing Big Data to support knowledge elicitation and decision-making, as well as providing information in a form that can be readily interpreted by a variety of audiences

Safety management theory and the military expeditionary organization: A critical theoretical reflection

Management of safety within organizations has become a key topic within safety science. Theorizing on this subject covers a diverse pallet of concepts such as “resilience” and “safety management systems”. Recent studies indicate that safety management theory has deficiencies. Our interpretation of these deficiencies is that much confusion originates from the issue that crucial meta-theoretical assumptions are mostly implicit or applied inconsistently. In particular, we argue that these meta-theoretical assumptions are of a systems theoretical nature. Therefore, we provide a framework that will be able to explicate and reflect on systems theoretical assumptions. With this framework, we analyze the ability of two frequently used safety management theories to tackle the problem of managing safety of Dutch military expeditionary organizations. This paper will show that inconsistent and implicit application of systems theoretical assumptions in these safety management theories results in problems to tackle such a practical problem adequately. We conclude with a reflection on the pros and cons of our framework. Also, we suggest particular meta-theoretical aspects that seem to be essential for applying safety management theory to organizations

Developments in the Safety Science Domain and in Safety Management From the 1970s Till the 1979 Near Disaster at Three Mile Island

Objective: What has been the influence of general management schools and safety research into causes of accidents and disasters on managing safety from 1970 till 1979? Method: The study was limited to original articles and documents, written in English or Dutch from the period under concern. For the Netherlands, the professional journal De Veiligheid (Safety) has been consulted. Results and conclusions: Dominant management approaches started with 1) the classical management starting from the 19th century, with scientific management from the start of the 20st century as a main component. During the interwar period 2) behavioural management started, based on behaviourism, followed by 3) quantitative management from the Second World War onwards. After the war 4) modern management became important. A company was seen as an open system, interacting with an external environment with external stakeholders. These schools management were not exclusive, but have existed in the period together. Early 20th century, the U.S. 'Safety First' movement was the starting point of this knowledge development on managing safety, with cost reduction and production efficiency as key drivers. Psychological models and metaphors explained accidents from ‘unsafe acts’. And safety was managed with training and selection of reckless workers, all in line with scientific management. Supported by behavioural management, this approach remained dominant for many years, even long after World War II. Influenced by quantitative management, potential and actual disasters after the war led to two approaches; loss prevention (up-scaling process industry) and reliability engineering (inherently dangerous processes in the aerospace and nuclear industries). The distinction between process safety and occupational safety became clear after the war, and the two developed into relatively independent domains. In occupational safety in the 1970s human errors thought to be symptoms of mismanagement. The term ‘safety management’ was introduced in scientific safety literature as well as concepts as loose, and tightly coupled processes, organizational culture, incubation of a disaster and mechanisms blinding organizations for portents of disaster scenarios. Loss prevention remained technically oriented. Till 1979 there was no clear relation with safety management. Reliability engineering, based on systems theory did have that relation with the MORT technique as a management audit. The Netherlands mainly followed Anglo-Saxon developments. Late 1970s, following international safety symposia in The Hague and Delft, independent research started in The Netherland

Driver Competence Performance Indicators Using OTMR

The current practice for assessing driver competence performance is in-cab riding by driver managers. However, this paper investigates whether real-world driving data extracted from on-train monitoring recorders data (OTMR) can be used to assess the driver performance. A number of indicators were used to evaluate the drivers’ performance. These include: their use of the emergency bypass switch, the driver's reminder appliance as well as the driver’s reaction time. A study case illustrated the applicability of OTMR data to estimate the proposed indicators, which suggests that the indicators can be useful in the driver management system in addition to the current indicators. Furthermore, the proposed indicators could be used to tailor the driver training schemes up to their individual needs and evaluate their effectiveness. They could even be used for improving driver competence performance and reducing crash involvement by revealing potentially detrimental driving performance

Using visual analytics to make sense of railway Close Calls

In the big data era, large and complex data sets will exceed scientists’ capacity to make sense of them in the traditional way. New approaches in data analysis, supported by computer science, will be necessary to address the problems that emerge with the rise of big data. The analysis of the Close Call database, which is a text-based database for near-miss reporting on the GB railways, provides a test case. The traditional analysis of Close Calls is time consuming and prone to differences in interpretation. This paper investigates the use of visual analytics techniques, based on network text analysis, to conduct data analysis and extract safety knowledge from 500 randomly selected Close Call records relating to worker slips, trips and falls. The results demonstrate a straightforward, yet effective, way to identify hazardous conditions without having to read each report individually. This opens up new ways to perform data analysis in safety science

Ontology network analysis for safety learning in the railway domain

Ontologies have been used in diverse areas such as Knowledge Management (KM), Artificial Intelligence (AI), Natural Language Processing (NLP) and Semantic Web as they allow software applications to integrate, query and reason about concepts and relations within a knowledge domain. For Big Data Risk Analysis (BDRA) in railways, ontologies are a key enabler for obtaining valuable insights into safety from the large amount of data available from the railway. Traditionally, the ontology building has been an entirely manual process that has required a considerable human effort and development time. During the last decade, the in-formation explosion due to the Internet and the need to develop large-scale methods to extract patterns in a systematic way, has given rise the research area of “ontology learning”. Despite recent research efforts, ontol-ogy learning systems are still struggling with extracting terms (words or multiple-words) from text-based data. This manuscript explores the benefits of visual analytics to support the construction of ontologies for a particular part of railway safety management: possessions. In railways, possession operations are the protection arrangements for engineering work that ensure track workers remain separated from moving trains. A network of terms from possession operations standards is represented to extract the concepts of the ontology that enable the safety learning from events related to possession operations

Big Data Risk Analysis for Railway Safety

Computer scientists are quite clear in their belief that the internet is coming of age. They have a firm belief that the enormous amounts of data floating around in the internet will unchain a management revolution of uncanny proportions. This revolution is referred to as ‘Big Data’. Yet, to date, the potential benefit of this revolution is scantily investigated for safety and risk management of the railways. This work reports about an investigation how Big Data can contribute to safety systems for the GB railways. The experience that is gained also sheds light on Big Data as a driver for change in the railway industry as a whole